一种基于残缺语言判断矩阵的群决策方法

本文针对具有残缺语言判断矩阵形式方案偏好信息的群决策问题,提出了一种决策分析方法.首先,阐述了二元语义的概念,并提出了一种扩展的二元语义有序加权平均(ETOWA)算子;然后,采用ETOWA算子集结具有残缺语言判断矩阵形式的方案偏好信息,可计算出每个方案优于其他方案的总体偏好程度,进而可得到所有方案的排序结果.最后,通过给出一个算例说明了本文提出方法的可行性和实用性.     

基于乘性一致性残缺互补判断矩阵的决策方法

对于满足乘性一致性的残缺互补判断矩阵的决策问题,提出了一种决策方法。首先把互补判断矩阵的乘性一致性定义进行了简化,得到了互补判断矩阵乘性一致性的另外几种表达形式;进一步得到了在已知n-1个特殊元素的条件下,残缺互补判断矩阵中缺失元素的补全方法;然后给出了残缺互补判断矩阵可接受的条件,以及矩阵的一致性检验及调整方法;基于残缺互补判断矩阵,给出了以下决策步骤:残缺互补判断矩阵的一致性检验及调整过程,补全缺失元素的迭代过程和最优方案择优过程。最后给出了一个实例,通过该实例的计算以及本文方法与已有方法的比较,证明了本文方法是简便和有效的。     

基于残缺互补判断矩阵的一种群决策模型

研究了基于残缺互补判断矩阵的群决策模型.首先,利用积型残缺一致性互补判断矩阵和互补判断矩阵的排序向量之间的偏差,建立并求解一个非线性规划模型,得到各专家残缺互补判断矩阵的排序向量.然后,通过最小化专家残缺互补判断矩阵的排序向量与专家群组排序向量的偏差,再次建立并求解一个非线性规划模型,得到反映出专家群组偏好的排序向量,从而得到了残缺互补判决矩阵的群决策模型.最后,通过算例说明了方法的可行性.     

区间数判断矩阵的一致性及权重计算

定义区间数判断矩阵的一致性可接受等概念,利用一组约束等式提取原区间数判断矩阵的全部一致性信息,并在此基础上建立非线性规划模型,提出一种新的权重计算方法,最后以算例说明其运用。     

基于COWA算子的不同类型残缺判断矩阵集结方法

本文研究了决策信息以残缺不确定判断矩阵形式给出的群体决策问题。将残缺不确定区间互反判断矩阵转化为残缺不确定区间互补判断矩阵。基于COWA算子,定义了期望值函数,把残缺不确定区间互反判断矩阵转化成残缺实判断矩阵进行排序,减少了决策信息的丢失。利用IOWA算子来集结群体判断矩阵。最后通过算例说明了该方法的可行性和有效性。     

基于残缺信息的直觉模糊判断矩阵群决策方法

直觉模糊判断模拟人类的决策过程、反映人类的经验和行为,在处理复杂不确定性决策问题方面具有较高的灵活性和实用性。在实际决策过程中,直觉模糊判断矩阵中的元素会有残缺。本文研究了残缺信息下直觉模糊群组判断矩阵的最优排序方法问题。利用直觉模糊判断的一致性条件,提出了残缺信息下直觉模糊群组判断的最小二乘排序模型,并将此模型推广到完全信息直觉模糊群组判断的情形。算例分析表明,本文的研究是有效可行的。     

基于不确定互补判断矩阵的多目标决策方法研究

针对多目标决策中两种不确定互补判断矩阵形式(区间数互补判断矩阵与三角模糊数互补判断矩阵),给出了各自的模型及其排序方法,并对一些方法进行了推广,提出了一些模型的新方法,为不确定互补判断矩阵排序方法的进一步研究奠定了基础.     

群决策中判断的一致性分析及方案排序

对群决策中基于区间数互补判断矩阵形式偏好信息的一致性问题进行了探讨。首先,给出了区间数互补判断矩阵相容性指标定义及性质;然后,通过定义群和个体判断的一致性指标,提出了群判断的一致性判别方法和不一致的协调方法;同时依据个体判断的一致性指标大小,即群成员评判水平的高低,给出了群偏好信息的集结方法;最后给出了由区间数互补判断矩阵导出方案排序权重的计算方法及应用算例。     

基于语言值2元组的多属性决策方法

利用2元组方法建立了一个语言值决策模型和LOW A算子模型,这两个模型具有可操作性强和语义明确等优点,特别是充分利用了语言值所含的信息,提高了决策结果的精度.最后,利用这两个模型给出了基于语言值的多属性群决策方法,同时给出一个应用实例.     

模糊残缺判断矩阵的修补

模糊判断矩阵是决策者在决策中所提供的一种重要的偏好信息,然而专家所给出的判断矩阵可能是带有残缺的.给出了模糊残缺判断矩阵中残存元素的极大一致独立组、残存元素组导出图、残缺矩阵的可接受性概念,并讨论了模糊残缺判断矩阵的极大一致独立组的导出图是树的条件,进而说明n阶模糊残缺矩阵中n-1个元素所导出的图是n阶树的话,模糊残缺判断矩阵即为可接受的.最后给出了一个可接受的残缺判断矩阵修补的算例.     

Multi-criteria semantic dominance: A linguistic decision aiding technique based on incomplete preference information

A linguistic decision aiding technique for multi-criteria decision is presented. We define a relation between alternatives as multi-criteria semantic dominance (MCSD). It adopts the similar ideal of the stochastic dominance by utilizing the partial information of the decision maker’s preference, which is only ordinal or partially cardinal. The MCSD rules based on three typical types of semanteme functions are introduced and proven. By using these rules, all the alternatives under consideration are divided into two mutually exclusive sets called efficient set and inefficient set. The decision maker who has such a semanteme function will never choose the alternative from the corresponding inefficient set as the optimal one. In such a way, when we analyze the linguistic decision information, the inherent fuzziness of preference can be handled and several controversial operations of the linguistic terms can be avoided. An example is also provided to illustrate the procedure of the proposed method.     

An integrated model-based interactive approach to FMAGDM with incomplete preference information

Incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations are very useful to express decision makers’ incomplete preferences over attributes or alternatives in the process of decision making under fuzzy environments. The aim of this paper is to investigate fuzzy multiple attribute group decision making problems where the attribute values are represented in intuitionistic fuzzy numbers and the information on attribute weights is provided by decision makers by means of one or some of the different preference structures, including weak ranking, strict ranking, difference ranking, multiple ranking, interval numbers, incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations. We transform all individual intuitionistic fuzzy decision matrices into the interval decision matrices and construct their expected decision matrices, and then aggregate all these expected decision matrices into a collective one. We establish an integrated model by unifying the collective decision matrix and all the given different structures of incomplete weight preference information, and develop an integrated model-based approach to interacting with the decision makers so as to adjust all the inconsistent incomplete fuzzy preference relations, inconsistent incomplete linguistic preference relations and inconsistent incomplete multiplicative preference relations into the ones with acceptable consistency. The developed approach can derive the attribute weights and the ranking of the alternatives directly from the integrated model, and thus it has the following prominent characteristics: (1) it does not need to construct the complete fuzzy preference relations, complete linguistic preference relations and complete multiplicative preference relations from the incomplete fuzzy preference relations, incomplete linguistic preference relations and incomplete multiplicative preference relations, respectively; (2) it does not need to unify the different structures of incomplete preferences, and thus can simplify the calculation and avoid distorting the given preference information; and (3) it can sufficiently reflect and adjust the subjective desirability of decision makers in the process of interaction. A practical example is also provided to illustrate the developed approach.     

On estimation of the number of classes based on incomplete information

For equiprobable multinomial distributions, the estimators for the unknown number of outcomes are constructed when not all frequencies are available. Traslated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 52–57, Perm. 1993.     

An inverse prospect theory-based algorithm in extended incomplete additive probabilistic linguistic preference relation environment and its application in financial products selection

Fuzzy Optimization and Decision Making - Selecting financial products is one of the most fundamental investment activities to both individuals and companies, and therefore it is very important to...     

A goal programming approach to group decision making based on multiplicative preference relations and fuzzy preference relations

This paper analyses the mechanisms through which binding finance constraints can induce debt-constrained firms to improve technical efficiency to guarantee positive profits. This hypothesis is tested on a sample of firms belonging to the Italian manufacturing. Technical efficiency scores are computed by estimating parametric production frontiers using the one stage approach as in Battese and Coelli [Battese, G., Coelli, T., 1995. A model for technical efficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20, 325–332]. The results support the hypothesis that a restriction in the availability of financial resources can affect positively efficiency.     

Parameter estimation in linear static systems based on weighted least-absolute value estimation

In this paper, we present a new method for estimating a parameter vector of which measurements are carried out; however, these measurements are subjected to noise. First, we briefly consider least-square estimation of such vector to obtain some well-known results. Then, we proceed to formulate the problem in the least-absolute value (LAV) sense and show that we can obtain a set of overdetermined equations for the components of the unknown vector. These equations are solved using the least-square approach to ascertain which points give the least residuals. Having gained that information, we set to zero a number of residuals equal to the rank of the matrixH. Let this rank bek; then, the number of points which satisfy the LAV solution identically isk; this is a requirement that the LAV solution must satisfy (Refs. 1, 2). Several examples are presented in the paper.This work was supported by the Natural Science and Engineering Research Council of Canada, Grant A4146.     

A comparative analysis of probabilistic linguistic preference relations and distributed preference relations for decision making

Fuzzy Optimization and Decision Making - When a decision-maker prefers to compare different alternatives in pairs to handle real situations, there are many different expression styles that can be...     

Multi-attribute group decision-making with multi-granularity linguistic assessment information: An improved approach based on deviation and TOPSIS

With respect to group decision-making problems with multi-granularity linguistic assessment information, a new approach is proposed. Firstly, the computational formulae are given in order to transform and unify the multi-granularity linguistic comparison matrices. Secondly, the method of standard and mean deviation is applied to determine the unknown attribute weights, and the weights of the decision makers will be determined by using the extended TOPSIS (technique for order preference by similarity to an ideal solution) method. Finally, based on the LWAA (linguistic weighted arithmetic averaging) operator, information on the preference provided by each decision maker is aggregated into the comprehensive evaluation value of each alternative, and the most desirable alternative is selected. The proposed approach expands the research in multi-attribute group decision-making with multi-granularity linguistic assessment information by both considering the weights of the attributes and decision makers, and objective weighting for them. A numerical example is given to illustrate the practicability and usefulness of the proposed approach.     

Sensory evaluation based on linguistic decision analysis

The evaluation processes are widely used for quality inspection, design, marketing exploitation and other fields in industrial companies. In many of these fields the items, products, designs, etc., are evaluated according to the knowledge acquired via human senses (sight, taste, touch, smell and hearing), in such cases, we talk about sensory evaluation, in it an important problem arises as it is the modelling and management of uncertain knowledge in the evaluation process, because the information acquired by our senses throughout human perceptions always involves uncertainty, vagueness and imprecision.The decision analysis techniques have been utilized in many evaluation processes, hence this paper proposes and shows the application of the linguistic decision analysis to sensory evaluation and its advantages, particularly based on the linguistic 2-tuple representation model, in order to model and manage consistently the uncertainty and vagueness of the information in this type of problems.